Improving regional weather forecasts with neural interpolation

• URL: http://arxiv.org/abs/2505.12040v1
• Date: Sat, 17 May 2025 15:05:09 GMT
• Title: Improving regional weather forecasts with neural interpolation
• Authors: James Jackaman, Oliver Sutton,
• Abstract summary: We design a neural operator to improve the boundary data for regional weather models.<n>In particular, we expose a methodology for approaching the problem through the study of a simplified model.<n>Our approach will exploit a combination of techniques from image super-resolution with convolutional neural networks (CNNs) and residual networks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper we design a neural interpolation operator to improve the boundary data for regional weather models, which is a challenging problem as we are required to map multi-scale dynamics between grid resolutions. In particular, we expose a methodology for approaching the problem through the study of a simplified model, with a view to generalise the results in this work to the dynamical core of regional weather models. Our approach will exploit a combination of techniques from image super-resolution with convolutional neural networks (CNNs) and residual networks, in addition to building the flow of atmospheric dynamics into the neural network

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